Tetracycline removal from wastewater via g-C3N4 loaded RSM-CCD-optimised hybrid photocatalytic membrane reactor
Bibliographic record
Abstract
In this study, a split-type photocatalytic membrane reactor (PMR), incorporating suspended graphitic carbon nitride (g-C3N4) as photocatalyst and a layered polymeric composite (using polyamide, polyethersulfone and polysulfone polymers) as a membrane was fabricated to remove tetracycline (TC) from aqueous solutions as the world's second most used and discharged antibiotic in wastewater. The photocatalyst was synthesised from melamine by ultrasonic-assisted thermal polymerisation method and, along with the membrane, was characterised using various methods, including Brunauer-Emmett-Teller analysis (BET), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), Field emission scanning electron microscopy (FESEM), and Ultraviolet-visible spectroscopy (UV-Vis). The PMR process was optimised, using Design-Expert software for tetracycline removal in terms of UV irradiation time, pH, photocatalyst loading, tetracycline concentration, and membrane separation iteration. It was revealed that a membrane-integrated reactor as a sustainable system could effectively produce clean water by simultaneous removal of tetracycline and photocatalyst from aqueous solution. The maximum removal of 94.8% was obtained at the tetracycline concentration of 22.16 ppm, pH of 9.78 with 0.56 g/L of photocatalyst in the irradiation time of 113.77 min after six times of passing membrane. The PMR system showed reasonable reusability by about a 25.8% drop in TC removal efficiency after seven cycles at optimal conditions. The outcomes demonstrate the promising performance of the proposed PMR system in tetracycline removal from water and suggest that it can be scaled as an effective approach for a sustainable supply of antibiotic-free clean water.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".